{
 "cells": [
  {
   "cell_type": "code",
   "id": "c12044962848e22c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:02.683696Z",
     "start_time": "2024-12-15T18:29:02.665701Z"
    }
   },
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications import InceptionV3, ResNet152\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, concatenate\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.python.keras.applications.resnet_v2 import ResNet152V2"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:02.714696Z",
     "start_time": "2024-12-15T18:29:02.698697Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_dir=r'D:\\ProgramData\\CDUT\\mv\\cw\\202118010101_MV_Coursework\\Resource\\MURA-v1.1\\train'\n",
    "vail_dir=r'D:\\ProgramData\\CDUT\\mv\\cw\\202118010101_MV_Coursework\\Resource\\MURA-v1.1\\valid'"
   ],
   "id": "eb72196102429b6f",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:05.658978Z",
     "start_time": "2024-12-15T18:29:02.731208Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "\n",
    "# 定义训练集和验证集的数据增强和归一化\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,  # 归一化到[0, 1]\n",
    "    rotation_range=20,  # 随机旋转角度\n",
    "    width_shift_range=0.2,  # 随机水平平移\n",
    "    height_shift_range=0.2,  # 随机垂直平移\n",
    "    shear_range=0.2,  # 随机错切变换\n",
    "    zoom_range=0.2,  # 随机缩放\n",
    "    horizontal_flip=True,  # 随机水平翻转\n",
    "    fill_mode='nearest'  # 填充模式\n",
    ")\n",
    "\n",
    "val_datagen = ImageDataGenerator(rescale=1./255)  # 只进行归一化\n",
    "\n",
    "# 生成训练数据\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(128, 128),  # InceptionV3的输入尺寸\n",
    "    batch_size=6,\n",
    "    class_mode='categorical'  # 使用one-hot编码\n",
    ")\n",
    "#生成测试数据集\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    vail_dir,\n",
    "    target_size=(128, 128),  # InceptionV3的输入尺寸\n",
    "    batch_size=6,\n",
    "    class_mode='categorical'  # 使用one-hot编码\n",
    ")\n",
    "\n",
    "# 生成验证数据\n",
    "validation_generator = val_datagen.flow_from_directory(\n",
    "    vail_dir,\n",
    "    target_size=(128, 128),  # InceptionV3的输入尺寸\n",
    "    batch_size=6,\n",
    "    class_mode='categorical'  # 使用one-hot编码\n",
    ")"
   ],
   "id": "8e930c678dc522bc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 36808 images belonging to 7 classes.\n",
      "Found 3197 images belonging to 7 classes.\n",
      "Found 3197 images belonging to 7 classes.\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:30:30.896426Z",
     "start_time": "2024-12-15T18:30:30.879423Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# 打印类别索引\n",
    "print(train_generator.class_indices)\n",
    "print(validation_generator.class_indices)\n"
   ],
   "id": "14f498cec4085ac5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'XR_ELBOW': 0, 'XR_FINGER': 1, 'XR_FOREARM': 2, 'XR_HAND': 3, 'XR_HUMERUS': 4, 'XR_SHOULDER': 5, 'XR_WRIST': 6}\n",
      "{'XR_ELBOW': 0, 'XR_FINGER': 1, 'XR_FOREARM': 2, 'XR_HAND': 3, 'XR_HUMERUS': 4, 'XR_SHOULDER': 5, 'XR_WRIST': 6}\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:30:34.227468Z",
     "start_time": "2024-12-15T18:30:34.211456Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "# 绘制训练历史\n",
    "def plot_history(history, model_name):\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['accuracy'], label='train accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='val accuracy')\n",
    "    plt.title(f'{model_name} Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend(loc='lower right')\n",
    "    \n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['loss'], label='train loss')\n",
    "    plt.plot(history.history['val_loss'], label='val loss')\n",
    "    plt.title(f'{model_name} Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend(loc='upper right')\n",
    "    \n",
    "    plt.show()"
   ],
   "id": "82bdb5c0c37e21ef",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:06.407625Z",
     "start_time": "2024-12-15T18:29:06.393442Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from lime import lime_image\n",
    "import numpy as np\n",
    "from skimage.segmentation import mark_boundaries\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def explain_image(img, Model):\n",
    "    explainer = lime_image.LimeImageExplainer()\n",
    "    explanation = explainer.explain_instance(\n",
    "        img.astype('double'),\n",
    "        Model.predict,\n",
    "        top_labels=5,\n",
    "        hide_color=0,\n",
    "        num_samples=1000\n",
    "    )\n",
    "\n",
    "    temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=False)\n",
    "    img_boundry1 = mark_boundaries(temp/255.0, mask)\n",
    "\n",
    "    plt.imshow(img_boundry1)\n",
    "    plt.show()\n",
    "\n",
    "# 获取一个验证样本\n",
    "img = validation_generator.next()[0][0]\n",
    "\n"
   ],
   "id": "4e44dada95ed42b",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:31:04.983393Z",
     "start_time": "2024-12-15T18:31:04.860193Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras.layers import Conv2D, Input, MaxPooling2D\n",
    "\n",
    "# 定义输入形状和类别数\n",
    "input_shape = Input(shape=(128,128,3)) # InceptionV3的输入形状\n",
    "num_classes = len(train_generator.class_indices)  # 类别数\n",
    "\n",
    "# 定义 Inception 模块 \n",
    "def inception_block(input_tensor, filter_1x1):\n",
    "    # First branch\n",
    "    branch_1_conv_1x1 = Conv2D(128, (1, 1), padding='same', activation='relu')(input_tensor)\n",
    "\n",
    "    # Second branch\n",
    "    branch_2_conv_1x1 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_tensor)\n",
    "    branch_2_conv_3x3 = Conv2D(192, (3, 3), padding='same', activation='relu')(branch_2_conv_1x1)\n",
    "    branch_2_conv_1x1 = Conv2D(filter_1x1, (1, 1), padding='same', activation='relu')(branch_2_conv_3x3)\n",
    "\n",
    "    # Third branch\n",
    "    branch_3_conv_1x1 = Conv2D(64, (1, 1), padding='same', activation='relu')(input_tensor)\n",
    "    branch_3_conv_5x5 = Conv2D(96, (5, 5), padding='same', activation='relu')(branch_3_conv_1x1)\n",
    "    branch_3_conv_1x1 = Conv2D(64, (1, 1), padding='same', activation='relu')(branch_3_conv_5x5)\n",
    "\n",
    "    branch_output = concatenate([branch_1_conv_1x1, branch_2_conv_1x1, branch_3_conv_1x1], axis=-1)\n",
    "    return branch_output\n",
    "\n",
    "x = Conv2D(64, (7,7), activation='relu')(input_shape)\n",
    "x = MaxPooling2D(2,2)(x)\n",
    "x = Conv2D(64, (3,3), activation='relu')(x)\n",
    "x = Conv2D(32, (1,1), activation='relu')(x)\n",
    "x = inception_block(x, 64)\n",
    "x = MaxPooling2D(2,2)(x)\n",
    "x = inception_block(x, 64)\n",
    "x = MaxPooling2D(2,2)(x)\n",
    "x = inception_block(x, 64)\n",
    "x = MaxPooling2D(2,2)(x)\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "output = Dense(7, activation='softmax')(x)\n",
    "inception_model = Model(inputs=input_shape, outputs=output)"
   ],
   "id": "3f05a2da45744665",
   "outputs": [],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:31:12.381563Z",
     "start_time": "2024-12-15T18:31:12.365566Z"
    }
   },
   "cell_type": "code",
   "source": "inception_model.summary()",
   "id": "bb3310b7bf6aff9b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 122, 122, 64) 9472        input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 61, 61, 64)   0           conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 59, 59, 64)   36928       max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 59, 59, 32)   2080        conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 59, 59, 64)   2112        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 59, 59, 64)   2112        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 59, 59, 192)  110784      conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 59, 59, 96)   153696      conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 59, 59, 128)  4224        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 59, 59, 64)   12352       conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 59, 59, 64)   6208        conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 59, 59, 256)  0           conv2d_27[0][0]                  \n",
      "                                                                 conv2d_30[0][0]                  \n",
      "                                                                 conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2D)  (None, 29, 29, 256)  0           concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 29, 29, 64)   16448       max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 29, 29, 64)   16448       max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 29, 29, 192)  110784      conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 29, 29, 96)   153696      conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 29, 29, 128)  32896       max_pooling2d_5[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 29, 29, 64)   12352       conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 29, 29, 64)   6208        conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 29, 29, 256)  0           conv2d_34[0][0]                  \n",
      "                                                                 conv2d_37[0][0]                  \n",
      "                                                                 conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2D)  (None, 14, 14, 256)  0           concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 14, 14, 64)   16448       max_pooling2d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 14, 14, 64)   16448       max_pooling2d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 14, 14, 192)  110784      conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 14, 14, 96)   153696      conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 14, 14, 128)  32896       max_pooling2d_6[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 14, 14, 64)   12352       conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 14, 14, 64)   6208        conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 14, 14, 256)  0           conv2d_41[0][0]                  \n",
      "                                                                 conv2d_44[0][0]                  \n",
      "                                                                 conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2D)  (None, 7, 7, 256)    0           concatenate_5[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_1 (Glo (None, 256)          0           max_pooling2d_7[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 7)            1799        global_average_pooling2d_1[0][0] \n",
      "==================================================================================================\n",
      "Total params: 1,039,431\n",
      "Trainable params: 1,039,431\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:31:16.136860Z",
     "start_time": "2024-12-15T18:31:16.122693Z"
    }
   },
   "cell_type": "code",
   "source": "inception_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])",
   "id": "a2ceda4383cbe829",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "\n",
    "print(\"Training InceptionV3 model...\")\n",
    "inception_model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    epochs=10\n",
    ")"
   ],
   "id": "1625dd69c0b41331",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "plot_history(inception_model.history, 'InceptionV3')",
   "id": "ecdd4fb2c78535a0"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "start_time": "2024-12-15T18:31:18.199690Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training InceptionV3 model...\n",
      "Epoch 1/10\n",
      "2818/6134 [============>.................] - ETA: 3:20 - loss: 1.6658 - accuracy: 0.3502"
     ]
    }
   ],
   "execution_count": null,
   "source": "explain_image(img, inception_model)",
   "id": "933d6ad8fe5acfcf"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.315210600Z",
     "start_time": "2024-12-14T10:23:06.429859Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "inception_model.save('inception_model.h5')\n",
    "save_dir='plots'\n",
    "if not os.path.exists(save_dir):\n",
    "    os.mkdir(save_dir)"
   ],
   "id": "23b99f13c628cca8",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:34:14.553054Z",
     "start_time": "2024-12-14T09:26:35.628696Z"
    }
   },
   "cell_type": "code",
   "outputs": [],
   "execution_count": 10,
   "source": [
    "\n",
    "import os\n",
    "from PIL import Image\n",
    "\n",
    "def check_images(directory):\n",
    "    for root, dirs, files in os.walk(directory):\n",
    "        for file in files:\n",
    "            try:\n",
    "                with Image.open(os.path.join(root, file)) as img:\n",
    "                       img.verify()  # 检查图像是否有效\n",
    "            except Exception as e:\n",
    "                   print(f\"Error with {os.path.join(root, file)}: {e}\")\n",
    "\n",
    "check_images(train_dir)\n",
    "check_images(vail_dir)\n",
    "   \n",
    "     "
   ],
   "id": "4d368b0a6d2378bd"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from keras.layers import ReLU, Add, BatchNormalization\n",
    "\n",
    "input_shape = Input(shape=(128,128,3))\n",
    "# 构建ResNet152模型\n",
    "def res_block(input_shape, filters, strides=1):\n",
    "    #\n",
    "    shortcut = input_shape\n",
    "\n",
    "    x = Conv2D(filters, (1,1), strides=1, padding='same')(input_shape)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = ReLU()(x)\n",
    "\n",
    "    x = Conv2D(filters, (3,3), padding='same')(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = ReLU()(x)\n",
    "\n",
    "    x = Conv2D(filters*4, (1,1), padding='same')(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    # Skip connection\n",
    "    if strides !=1 or shortcut.shape[-1]!= filters*4:\n",
    "        shortcut = Conv2D(filters*4, (1,1), strides=strides, padding='same')(shortcut)\n",
    "        shortcut = BatchNormalization()(shortcut)\n",
    "    # Add the shortcut to the main path\n",
    "    x = Add()([x, shortcut])\n",
    "    x = ReLU()(x)\n",
    "    \n",
    "    return x\n",
    "# Build a simple ResNet with two residual blocks\n",
    "x = Conv2D(64, (7,7), strides=2, padding='same')(input_shape)\n",
    "x = BatchNormalization()(x)\n",
    "x = ReLU()(x)\n",
    "x = MaxPooling2D(2,2)(x)\n",
    "x = res_block(x, filters=64, strides=1)\n",
    "x = res_block(x, filters=64, strides=1)\n",
    "x = MaxPooling2D(2,2)(x)\n",
    "x = res_block(x, filters=128, strides=1)\n",
    "x = res_block(x, filters=128, strides=1)\n",
    "x = MaxPooling2D(2,2)(x)\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "output = Dense(7, activation='softmax')(x)\n",
    "resnet_model = Model(inputs=input_shape, outputs=output)\n"
   ],
   "id": "5cfc342fb11a9546",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "\n",
    "resnet_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "resnet_model.summary()"
   ],
   "id": "b4364e6ff65b2fa1"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:34:14.554054500Z",
     "start_time": "2024-12-15T02:18:03.739047Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet152_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "234700800/234698864 [==============================] - 145s 1us/step\n",
      "234708992/234698864 [==============================] - 145s 1us/step\n",
      "Training ResNet50 model...\n",
      "Epoch 1/10\n",
      "6134/6134 [==============================] - 512s 82ms/step - loss: 1.0645 - accuracy: 0.6194 - val_loss: 0.8903 - val_accuracy: 0.7049\n",
      "Epoch 2/10\n",
      "6134/6134 [==============================] - 478s 78ms/step - loss: 0.4653 - accuracy: 0.8458 - val_loss: 0.7108 - val_accuracy: 0.7622\n",
      "Epoch 3/10\n",
      "6134/6134 [==============================] - 481s 78ms/step - loss: 0.3365 - accuracy: 0.8922 - val_loss: 0.3934 - val_accuracy: 0.8703\n",
      "Epoch 4/10\n",
      "6134/6134 [==============================] - 489s 80ms/step - loss: 0.2793 - accuracy: 0.9104 - val_loss: 1.2008 - val_accuracy: 0.6880\n",
      "Epoch 5/10\n",
      "6134/6134 [==============================] - 495s 81ms/step - loss: 0.2502 - accuracy: 0.9209 - val_loss: 0.6445 - val_accuracy: 0.8289\n",
      "Epoch 6/10\n",
      "6134/6134 [==============================] - 501s 82ms/step - loss: 0.2288 - accuracy: 0.9286 - val_loss: 1.3356 - val_accuracy: 0.6147\n",
      "Epoch 7/10\n",
      "6134/6134 [==============================] - 498s 81ms/step - loss: 0.2176 - accuracy: 0.9335 - val_loss: 0.3132 - val_accuracy: 0.9142\n",
      "Epoch 8/10\n",
      "6134/6134 [==============================] - 501s 82ms/step - loss: 0.2008 - accuracy: 0.9387 - val_loss: 0.5003 - val_accuracy: 0.8659\n",
      "Epoch 9/10\n",
      "6134/6134 [==============================] - 501s 82ms/step - loss: 0.1952 - accuracy: 0.9405 - val_loss: 0.9771 - val_accuracy: 0.6942\n",
      "Epoch 10/10\n",
      "6134/6134 [==============================] - 502s 82ms/step - loss: 0.1866 - accuracy: 0.9428 - val_loss: 0.2744 - val_accuracy: 0.9239\n",
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 305, 305, 3)  0           input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1_conv (Conv2D)             (None, 150, 150, 64) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv1_bn (BatchNormalization)   (None, 150, 150, 64) 256         conv1_conv[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1_relu (Activation)         (None, 150, 150, 64) 0           conv1_bn[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 152, 152, 64) 0           conv1_relu[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pool (MaxPooling2D)       (None, 75, 75, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_conv (Conv2D)    (None, 75, 75, 64)   4160        pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_1_relu (Activation (None, 75, 75, 64)   0           conv2_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_conv (Conv2D)    (None, 75, 75, 64)   36928       conv2_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_2_relu (Activation (None, 75, 75, 64)   0           conv2_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_conv (Conv2D)    (None, 75, 75, 256)  16640       pool1_pool[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_conv (Conv2D)    (None, 75, 75, 256)  16640       conv2_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_0_bn (BatchNormali (None, 75, 75, 256)  1024        conv2_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_3_bn (BatchNormali (None, 75, 75, 256)  1024        conv2_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_add (Add)          (None, 75, 75, 256)  0           conv2_block1_0_bn[0][0]          \n",
      "                                                                 conv2_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block1_out (Activation)   (None, 75, 75, 256)  0           conv2_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_conv (Conv2D)    (None, 75, 75, 64)   16448       conv2_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_1_relu (Activation (None, 75, 75, 64)   0           conv2_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_conv (Conv2D)    (None, 75, 75, 64)   36928       conv2_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_2_relu (Activation (None, 75, 75, 64)   0           conv2_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_conv (Conv2D)    (None, 75, 75, 256)  16640       conv2_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_3_bn (BatchNormali (None, 75, 75, 256)  1024        conv2_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_add (Add)          (None, 75, 75, 256)  0           conv2_block1_out[0][0]           \n",
      "                                                                 conv2_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block2_out (Activation)   (None, 75, 75, 256)  0           conv2_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_conv (Conv2D)    (None, 75, 75, 64)   16448       conv2_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_1_relu (Activation (None, 75, 75, 64)   0           conv2_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_conv (Conv2D)    (None, 75, 75, 64)   36928       conv2_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_bn (BatchNormali (None, 75, 75, 64)   256         conv2_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_2_relu (Activation (None, 75, 75, 64)   0           conv2_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_conv (Conv2D)    (None, 75, 75, 256)  16640       conv2_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_3_bn (BatchNormali (None, 75, 75, 256)  1024        conv2_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_add (Add)          (None, 75, 75, 256)  0           conv2_block2_out[0][0]           \n",
      "                                                                 conv2_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv2_block3_out (Activation)   (None, 75, 75, 256)  0           conv2_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_conv (Conv2D)    (None, 38, 38, 128)  32896       conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_1_relu (Activation (None, 38, 38, 128)  0           conv3_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_2_relu (Activation (None, 38, 38, 128)  0           conv3_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_conv (Conv2D)    (None, 38, 38, 512)  131584      conv2_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_0_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_add (Add)          (None, 38, 38, 512)  0           conv3_block1_0_bn[0][0]          \n",
      "                                                                 conv3_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block1_out (Activation)   (None, 38, 38, 512)  0           conv3_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_1_relu (Activation (None, 38, 38, 128)  0           conv3_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_2_relu (Activation (None, 38, 38, 128)  0           conv3_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_add (Add)          (None, 38, 38, 512)  0           conv3_block1_out[0][0]           \n",
      "                                                                 conv3_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block2_out (Activation)   (None, 38, 38, 512)  0           conv3_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_1_relu (Activation (None, 38, 38, 128)  0           conv3_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_2_relu (Activation (None, 38, 38, 128)  0           conv3_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_add (Add)          (None, 38, 38, 512)  0           conv3_block2_out[0][0]           \n",
      "                                                                 conv3_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block3_out (Activation)   (None, 38, 38, 512)  0           conv3_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_1_relu (Activation (None, 38, 38, 128)  0           conv3_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_2_relu (Activation (None, 38, 38, 128)  0           conv3_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_add (Add)          (None, 38, 38, 512)  0           conv3_block3_out[0][0]           \n",
      "                                                                 conv3_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block4_out (Activation)   (None, 38, 38, 512)  0           conv3_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_1_relu (Activation (None, 38, 38, 128)  0           conv3_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_2_relu (Activation (None, 38, 38, 128)  0           conv3_block5_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block5_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block5_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_add (Add)          (None, 38, 38, 512)  0           conv3_block4_out[0][0]           \n",
      "                                                                 conv3_block5_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block5_out (Activation)   (None, 38, 38, 512)  0           conv3_block5_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block5_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_1_relu (Activation (None, 38, 38, 128)  0           conv3_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_2_relu (Activation (None, 38, 38, 128)  0           conv3_block6_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block6_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block6_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_add (Add)          (None, 38, 38, 512)  0           conv3_block5_out[0][0]           \n",
      "                                                                 conv3_block6_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block6_out (Activation)   (None, 38, 38, 512)  0           conv3_block6_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_1_relu (Activation (None, 38, 38, 128)  0           conv3_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_2_relu (Activation (None, 38, 38, 128)  0           conv3_block7_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block7_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block7_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_add (Add)          (None, 38, 38, 512)  0           conv3_block6_out[0][0]           \n",
      "                                                                 conv3_block7_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block7_out (Activation)   (None, 38, 38, 512)  0           conv3_block7_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_conv (Conv2D)    (None, 38, 38, 128)  65664       conv3_block7_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_1_relu (Activation (None, 38, 38, 128)  0           conv3_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_2_conv (Conv2D)    (None, 38, 38, 128)  147584      conv3_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_2_bn (BatchNormali (None, 38, 38, 128)  512         conv3_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_2_relu (Activation (None, 38, 38, 128)  0           conv3_block8_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_3_conv (Conv2D)    (None, 38, 38, 512)  66048       conv3_block8_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_3_bn (BatchNormali (None, 38, 38, 512)  2048        conv3_block8_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_add (Add)          (None, 38, 38, 512)  0           conv3_block7_out[0][0]           \n",
      "                                                                 conv3_block8_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv3_block8_out (Activation)   (None, 38, 38, 512)  0           conv3_block8_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_conv (Conv2D)    (None, 19, 19, 256)  131328      conv3_block8_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_1_relu (Activation (None, 19, 19, 256)  0           conv4_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_2_relu (Activation (None, 19, 19, 256)  0           conv4_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_conv (Conv2D)    (None, 19, 19, 1024) 525312      conv3_block8_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_0_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_add (Add)          (None, 19, 19, 1024) 0           conv4_block1_0_bn[0][0]          \n",
      "                                                                 conv4_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block1_out (Activation)   (None, 19, 19, 1024) 0           conv4_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_1_relu (Activation (None, 19, 19, 256)  0           conv4_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_2_relu (Activation (None, 19, 19, 256)  0           conv4_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_add (Add)          (None, 19, 19, 1024) 0           conv4_block1_out[0][0]           \n",
      "                                                                 conv4_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block2_out (Activation)   (None, 19, 19, 1024) 0           conv4_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_1_relu (Activation (None, 19, 19, 256)  0           conv4_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_2_relu (Activation (None, 19, 19, 256)  0           conv4_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_add (Add)          (None, 19, 19, 1024) 0           conv4_block2_out[0][0]           \n",
      "                                                                 conv4_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block3_out (Activation)   (None, 19, 19, 1024) 0           conv4_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block4_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_1_relu (Activation (None, 19, 19, 256)  0           conv4_block4_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block4_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block4_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_2_relu (Activation (None, 19, 19, 256)  0           conv4_block4_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_add (Add)          (None, 19, 19, 1024) 0           conv4_block3_out[0][0]           \n",
      "                                                                 conv4_block4_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block4_out (Activation)   (None, 19, 19, 1024) 0           conv4_block4_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block4_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block5_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_1_relu (Activation (None, 19, 19, 256)  0           conv4_block5_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block5_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block5_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_2_relu (Activation (None, 19, 19, 256)  0           conv4_block5_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_add (Add)          (None, 19, 19, 1024) 0           conv4_block4_out[0][0]           \n",
      "                                                                 conv4_block5_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block5_out (Activation)   (None, 19, 19, 1024) 0           conv4_block5_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block5_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block6_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_1_relu (Activation (None, 19, 19, 256)  0           conv4_block6_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block6_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block6_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_2_relu (Activation (None, 19, 19, 256)  0           conv4_block6_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_add (Add)          (None, 19, 19, 1024) 0           conv4_block5_out[0][0]           \n",
      "                                                                 conv4_block6_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block6_out (Activation)   (None, 19, 19, 1024) 0           conv4_block6_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block6_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block7_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_1_relu (Activation (None, 19, 19, 256)  0           conv4_block7_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block7_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block7_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_2_relu (Activation (None, 19, 19, 256)  0           conv4_block7_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block7_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block7_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_add (Add)          (None, 19, 19, 1024) 0           conv4_block6_out[0][0]           \n",
      "                                                                 conv4_block7_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block7_out (Activation)   (None, 19, 19, 1024) 0           conv4_block7_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block7_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block8_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_1_relu (Activation (None, 19, 19, 256)  0           conv4_block8_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block8_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block8_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_2_relu (Activation (None, 19, 19, 256)  0           conv4_block8_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block8_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block8_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_add (Add)          (None, 19, 19, 1024) 0           conv4_block7_out[0][0]           \n",
      "                                                                 conv4_block8_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block8_out (Activation)   (None, 19, 19, 1024) 0           conv4_block8_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_conv (Conv2D)    (None, 19, 19, 256)  262400      conv4_block8_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block9_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_1_relu (Activation (None, 19, 19, 256)  0           conv4_block9_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_2_conv (Conv2D)    (None, 19, 19, 256)  590080      conv4_block9_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_2_bn (BatchNormali (None, 19, 19, 256)  1024        conv4_block9_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_2_relu (Activation (None, 19, 19, 256)  0           conv4_block9_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_3_conv (Conv2D)    (None, 19, 19, 1024) 263168      conv4_block9_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_3_bn (BatchNormali (None, 19, 19, 1024) 4096        conv4_block9_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_add (Add)          (None, 19, 19, 1024) 0           conv4_block8_out[0][0]           \n",
      "                                                                 conv4_block9_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block9_out (Activation)   (None, 19, 19, 1024) 0           conv4_block9_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block9_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block10_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block10_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block10_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block10_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block10_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block10_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block10_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_add (Add)         (None, 19, 19, 1024) 0           conv4_block9_out[0][0]           \n",
      "                                                                 conv4_block10_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block10_out (Activation)  (None, 19, 19, 1024) 0           conv4_block10_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block10_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block11_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block11_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block11_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block11_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block11_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block11_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block11_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_add (Add)         (None, 19, 19, 1024) 0           conv4_block10_out[0][0]          \n",
      "                                                                 conv4_block11_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block11_out (Activation)  (None, 19, 19, 1024) 0           conv4_block11_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block11_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block12_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block12_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block12_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block12_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block12_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block12_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block12_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_add (Add)         (None, 19, 19, 1024) 0           conv4_block11_out[0][0]          \n",
      "                                                                 conv4_block12_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block12_out (Activation)  (None, 19, 19, 1024) 0           conv4_block12_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block12_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block13_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block13_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block13_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block13_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block13_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block13_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block13_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_add (Add)         (None, 19, 19, 1024) 0           conv4_block12_out[0][0]          \n",
      "                                                                 conv4_block13_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block13_out (Activation)  (None, 19, 19, 1024) 0           conv4_block13_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block13_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block14_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block14_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block14_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block14_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block14_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block14_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block14_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_add (Add)         (None, 19, 19, 1024) 0           conv4_block13_out[0][0]          \n",
      "                                                                 conv4_block14_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block14_out (Activation)  (None, 19, 19, 1024) 0           conv4_block14_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block14_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block15_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block15_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block15_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block15_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block15_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block15_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block15_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_add (Add)         (None, 19, 19, 1024) 0           conv4_block14_out[0][0]          \n",
      "                                                                 conv4_block15_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block15_out (Activation)  (None, 19, 19, 1024) 0           conv4_block15_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block15_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block16_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block16_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block16_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block16_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block16_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block16_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block16_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_add (Add)         (None, 19, 19, 1024) 0           conv4_block15_out[0][0]          \n",
      "                                                                 conv4_block16_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block16_out (Activation)  (None, 19, 19, 1024) 0           conv4_block16_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block16_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block17_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block17_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block17_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block17_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block17_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block17_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block17_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_add (Add)         (None, 19, 19, 1024) 0           conv4_block16_out[0][0]          \n",
      "                                                                 conv4_block17_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block17_out (Activation)  (None, 19, 19, 1024) 0           conv4_block17_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block17_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block18_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block18_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block18_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block18_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block18_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block18_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block18_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_add (Add)         (None, 19, 19, 1024) 0           conv4_block17_out[0][0]          \n",
      "                                                                 conv4_block18_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block18_out (Activation)  (None, 19, 19, 1024) 0           conv4_block18_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block18_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block19_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block19_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block19_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block19_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block19_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block19_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block19_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_add (Add)         (None, 19, 19, 1024) 0           conv4_block18_out[0][0]          \n",
      "                                                                 conv4_block19_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block19_out (Activation)  (None, 19, 19, 1024) 0           conv4_block19_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block19_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block20_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block20_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block20_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block20_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block20_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block20_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block20_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_add (Add)         (None, 19, 19, 1024) 0           conv4_block19_out[0][0]          \n",
      "                                                                 conv4_block20_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block20_out (Activation)  (None, 19, 19, 1024) 0           conv4_block20_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block20_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block21_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block21_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block21_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block21_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block21_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block21_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block21_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_add (Add)         (None, 19, 19, 1024) 0           conv4_block20_out[0][0]          \n",
      "                                                                 conv4_block21_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block21_out (Activation)  (None, 19, 19, 1024) 0           conv4_block21_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block21_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block22_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block22_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block22_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block22_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block22_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block22_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block22_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_add (Add)         (None, 19, 19, 1024) 0           conv4_block21_out[0][0]          \n",
      "                                                                 conv4_block22_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block22_out (Activation)  (None, 19, 19, 1024) 0           conv4_block22_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block22_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block23_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block23_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block23_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block23_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block23_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block23_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block23_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_add (Add)         (None, 19, 19, 1024) 0           conv4_block22_out[0][0]          \n",
      "                                                                 conv4_block23_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block23_out (Activation)  (None, 19, 19, 1024) 0           conv4_block23_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block23_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block24_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block24_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block24_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block24_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block24_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block24_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block24_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_add (Add)         (None, 19, 19, 1024) 0           conv4_block23_out[0][0]          \n",
      "                                                                 conv4_block24_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block24_out (Activation)  (None, 19, 19, 1024) 0           conv4_block24_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block24_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block25_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block25_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block25_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block25_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block25_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block25_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block25_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_add (Add)         (None, 19, 19, 1024) 0           conv4_block24_out[0][0]          \n",
      "                                                                 conv4_block25_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block25_out (Activation)  (None, 19, 19, 1024) 0           conv4_block25_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block25_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block26_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block26_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block26_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block26_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block26_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block26_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block26_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_add (Add)         (None, 19, 19, 1024) 0           conv4_block25_out[0][0]          \n",
      "                                                                 conv4_block26_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block26_out (Activation)  (None, 19, 19, 1024) 0           conv4_block26_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block26_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block27_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block27_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block27_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block27_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block27_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block27_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block27_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_add (Add)         (None, 19, 19, 1024) 0           conv4_block26_out[0][0]          \n",
      "                                                                 conv4_block27_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block27_out (Activation)  (None, 19, 19, 1024) 0           conv4_block27_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block27_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block28_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block28_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block28_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block28_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block28_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block28_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block28_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_add (Add)         (None, 19, 19, 1024) 0           conv4_block27_out[0][0]          \n",
      "                                                                 conv4_block28_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block28_out (Activation)  (None, 19, 19, 1024) 0           conv4_block28_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block28_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block29_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block29_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block29_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block29_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block29_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block29_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block29_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_add (Add)         (None, 19, 19, 1024) 0           conv4_block28_out[0][0]          \n",
      "                                                                 conv4_block29_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block29_out (Activation)  (None, 19, 19, 1024) 0           conv4_block29_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block29_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block30_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block30_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block30_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block30_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block30_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block30_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block30_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_add (Add)         (None, 19, 19, 1024) 0           conv4_block29_out[0][0]          \n",
      "                                                                 conv4_block30_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block30_out (Activation)  (None, 19, 19, 1024) 0           conv4_block30_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block30_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block31_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block31_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block31_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block31_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block31_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block31_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block31_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_add (Add)         (None, 19, 19, 1024) 0           conv4_block30_out[0][0]          \n",
      "                                                                 conv4_block31_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block31_out (Activation)  (None, 19, 19, 1024) 0           conv4_block31_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block31_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block32_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block32_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block32_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block32_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block32_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block32_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block32_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_add (Add)         (None, 19, 19, 1024) 0           conv4_block31_out[0][0]          \n",
      "                                                                 conv4_block32_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block32_out (Activation)  (None, 19, 19, 1024) 0           conv4_block32_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block32_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block33_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block33_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block33_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block33_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block33_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block33_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block33_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_add (Add)         (None, 19, 19, 1024) 0           conv4_block32_out[0][0]          \n",
      "                                                                 conv4_block33_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block33_out (Activation)  (None, 19, 19, 1024) 0           conv4_block33_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block33_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block34_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block34_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block34_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block34_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block34_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block34_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block34_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_add (Add)         (None, 19, 19, 1024) 0           conv4_block33_out[0][0]          \n",
      "                                                                 conv4_block34_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block34_out (Activation)  (None, 19, 19, 1024) 0           conv4_block34_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block34_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block35_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block35_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block35_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block35_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block35_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block35_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block35_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_add (Add)         (None, 19, 19, 1024) 0           conv4_block34_out[0][0]          \n",
      "                                                                 conv4_block35_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block35_out (Activation)  (None, 19, 19, 1024) 0           conv4_block35_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_1_conv (Conv2D)   (None, 19, 19, 256)  262400      conv4_block35_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_1_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block36_1_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_1_relu (Activatio (None, 19, 19, 256)  0           conv4_block36_1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_2_conv (Conv2D)   (None, 19, 19, 256)  590080      conv4_block36_1_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_2_bn (BatchNormal (None, 19, 19, 256)  1024        conv4_block36_2_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_2_relu (Activatio (None, 19, 19, 256)  0           conv4_block36_2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_3_conv (Conv2D)   (None, 19, 19, 1024) 263168      conv4_block36_2_relu[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_3_bn (BatchNormal (None, 19, 19, 1024) 4096        conv4_block36_3_conv[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_add (Add)         (None, 19, 19, 1024) 0           conv4_block35_out[0][0]          \n",
      "                                                                 conv4_block36_3_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "conv4_block36_out (Activation)  (None, 19, 19, 1024) 0           conv4_block36_add[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_conv (Conv2D)    (None, 10, 10, 512)  524800      conv4_block36_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block1_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_1_relu (Activation (None, 10, 10, 512)  0           conv5_block1_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_conv (Conv2D)    (None, 10, 10, 512)  2359808     conv5_block1_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block1_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_2_relu (Activation (None, 10, 10, 512)  0           conv5_block1_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_conv (Conv2D)    (None, 10, 10, 2048) 2099200     conv4_block36_out[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_conv (Conv2D)    (None, 10, 10, 2048) 1050624     conv5_block1_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_0_bn (BatchNormali (None, 10, 10, 2048) 8192        conv5_block1_0_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_3_bn (BatchNormali (None, 10, 10, 2048) 8192        conv5_block1_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_add (Add)          (None, 10, 10, 2048) 0           conv5_block1_0_bn[0][0]          \n",
      "                                                                 conv5_block1_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block1_out (Activation)   (None, 10, 10, 2048) 0           conv5_block1_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_conv (Conv2D)    (None, 10, 10, 512)  1049088     conv5_block1_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block2_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_1_relu (Activation (None, 10, 10, 512)  0           conv5_block2_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_conv (Conv2D)    (None, 10, 10, 512)  2359808     conv5_block2_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block2_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_2_relu (Activation (None, 10, 10, 512)  0           conv5_block2_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_conv (Conv2D)    (None, 10, 10, 2048) 1050624     conv5_block2_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_3_bn (BatchNormali (None, 10, 10, 2048) 8192        conv5_block2_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_add (Add)          (None, 10, 10, 2048) 0           conv5_block1_out[0][0]           \n",
      "                                                                 conv5_block2_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block2_out (Activation)   (None, 10, 10, 2048) 0           conv5_block2_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_conv (Conv2D)    (None, 10, 10, 512)  1049088     conv5_block2_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block3_1_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_1_relu (Activation (None, 10, 10, 512)  0           conv5_block3_1_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_conv (Conv2D)    (None, 10, 10, 512)  2359808     conv5_block3_1_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_bn (BatchNormali (None, 10, 10, 512)  2048        conv5_block3_2_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_2_relu (Activation (None, 10, 10, 512)  0           conv5_block3_2_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_conv (Conv2D)    (None, 10, 10, 2048) 1050624     conv5_block3_2_relu[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_3_bn (BatchNormali (None, 10, 10, 2048) 8192        conv5_block3_3_conv[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_add (Add)          (None, 10, 10, 2048) 0           conv5_block2_out[0][0]           \n",
      "                                                                 conv5_block3_3_bn[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "conv5_block3_out (Activation)   (None, 10, 10, 2048) 0           conv5_block3_add[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_2 (Glo (None, 2048)         0           conv5_block3_out[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 1024)         2098176     global_average_pooling2d_2[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 7)            7175        dense_4[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 60,476,295\n",
      "Trainable params: 60,324,871\n",
      "Non-trainable params: 151,424\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "execution_count": 21,
   "source": [
    "# 训练ResNet50模型\n",
    "print(\"Training ResNet50 model...\")\n",
    "resnet_model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    epochs=10\n",
    ")\n"
   ],
   "id": "76d617d54fe50075"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "plot_history(resnet_model.history, 'ResNet')",
   "id": "e7d9c7ae50a97d88",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.315210600Z",
     "start_time": "2024-12-15T03:57:13.479142Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 26,
   "source": "explain_image(img, resnet_model)",
   "id": "initial_id"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.316213900Z",
     "start_time": "2024-12-15T03:56:58.555708Z"
    }
   },
   "cell_type": "code",
   "source": "resnet_model.save('resnet_model.h5')",
   "id": "684fbff12cfec646",
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Input, DepthwiseConv2D, BatchNormalization, Activation, GlobalAveragePooling2D, Dense\n",
    "\n",
    "def dws_block(input_shape, filters, strides):\n",
    "    #\n",
    "    x = DepthwiseConv2D((3,3), strides=strides, padding='same', use_bias=False)(input_shape)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = ReLU()(x)\n",
    "    x = Conv2D(filters, (1,1), strides=strides, padding='same', use_bias=False)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = ReLU()(x)\n",
    "    return x\n",
    "\n",
    "# 定义输入形状和类别数\n",
    "\n",
    "input_shape = Input(shape=(128,128,3))\n",
    "\n",
    "# Build a simple ResNet with two residual blocks\n",
    "x = Conv2D(32, (3,3), strides=2, padding='same')(input_shape)\n",
    "x = BatchNormalization()(x)\n",
    "x = ReLU()(x)\n",
    "x = dws_block(x, filters=32, strides=1)\n",
    "x = dws_block(x, filters=64, strides=2)\n",
    "x = dws_block(x, filters=128, strides=1)\n",
    "x = dws_block(x, filters=256, strides=1)\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "output = Dense(7, activation='softmax')(x)\n",
    "depthwise_separable_model=Model(inputs=input_shape, outputs=output)"
   ],
   "id": "948b72c62fa19e62",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "depthwise_separable_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])",
   "id": "e2d9659c515bce45"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "depthwise_separable_model.summary()",
   "id": "33ea43144ad4ce59"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.316213900Z",
     "start_time": "2024-12-15T03:57:37.568817Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Depthwise Separable Convolution model...\n",
      "Epoch 1/10\n",
      "6134/6134 [==============================] - 185s 30ms/step - loss: 1.4589 - accuracy: 0.4590 - val_loss: 1.6457 - val_accuracy: 0.4712\n",
      "Epoch 2/10\n",
      "6134/6134 [==============================] - 166s 27ms/step - loss: 1.2150 - accuracy: 0.5611 - val_loss: 5.0660 - val_accuracy: 0.2227\n",
      "Epoch 3/10\n",
      "6134/6134 [==============================] - 166s 27ms/step - loss: 1.0658 - accuracy: 0.6200 - val_loss: 11.1181 - val_accuracy: 0.1920\n",
      "Epoch 4/10\n",
      "6134/6134 [==============================] - 177s 29ms/step - loss: 0.9583 - accuracy: 0.6623 - val_loss: 1.6181 - val_accuracy: 0.4505\n",
      "Epoch 5/10\n",
      "6134/6134 [==============================] - 173s 28ms/step - loss: 0.8938 - accuracy: 0.6868 - val_loss: 3.0836 - val_accuracy: 0.3716\n",
      "Epoch 6/10\n",
      "6134/6134 [==============================] - 174s 28ms/step - loss: 0.8376 - accuracy: 0.7050 - val_loss: 1.5999 - val_accuracy: 0.5652\n",
      "Epoch 7/10\n",
      "6134/6134 [==============================] - 175s 29ms/step - loss: 0.8016 - accuracy: 0.7219 - val_loss: 1.8015 - val_accuracy: 0.5094\n",
      "Epoch 8/10\n",
      "6134/6134 [==============================] - 175s 28ms/step - loss: 0.7674 - accuracy: 0.7323 - val_loss: 1.6399 - val_accuracy: 0.5222\n",
      "Epoch 9/10\n",
      "6134/6134 [==============================] - 175s 29ms/step - loss: 0.7400 - accuracy: 0.7428 - val_loss: 1.2434 - val_accuracy: 0.6350\n",
      "Epoch 10/10\n",
      "6134/6134 [==============================] - 175s 29ms/step - loss: 0.7161 - accuracy: 0.7517 - val_loss: 2.0838 - val_accuracy: 0.4395\n"
     ]
    }
   ],
   "execution_count": 27,
   "source": [
    "# 训练模型\n",
    "print(\"Training Depthwise Separable Convolution model...\")\n",
    "history = depthwise_separable_model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    epochs=10\n",
    ")"
   ],
   "id": "bb03bc642c8d7d51"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "# 绘制训练历史\n",
    "plot_history(history, 'Depthwise Separable Convolution')"
   ],
   "id": "d6c1f0c53b6f212e",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.316213900Z",
     "start_time": "2024-12-15T04:26:38.889147Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 28,
   "source": "explain_image(img, depthwise_separable_model)",
   "id": "c622e9da66268acd"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.316213900Z",
     "start_time": "2024-12-15T04:26:39.094519Z"
    }
   },
   "cell_type": "code",
   "source": "depthwise_separable_model.save('depthwise_separable_model.h5')",
   "id": "10334db6a8b33599",
   "outputs": [],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.316213900Z",
     "start_time": "2024-12-15T04:26:39.110141Z"
    }
   },
   "cell_type": "code",
   "source": "#集成模型\n",
   "id": "5a1802f0e2218779",
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.316213900Z",
     "start_time": "2024-12-15T05:32:52.634533Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "b1d3130313c4d73a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training InceptionV3 with SE Block model...\n",
      "Epoch 1/10\n",
      "6134/6134 [==============================] - 283s 45ms/step - loss: 0.9113 - accuracy: 0.7028 - val_loss: 0.6560 - val_accuracy: 0.7851\n",
      "Epoch 2/10\n",
      "6134/6134 [==============================] - 257s 42ms/step - loss: 0.4773 - accuracy: 0.8535 - val_loss: 0.4550 - val_accuracy: 0.8665\n",
      "Epoch 3/10\n",
      "6134/6134 [==============================] - 256s 42ms/step - loss: 0.3714 - accuracy: 0.8891 - val_loss: 0.4172 - val_accuracy: 0.8628\n",
      "Epoch 4/10\n",
      "6134/6134 [==============================] - 260s 42ms/step - loss: 0.3153 - accuracy: 0.9055 - val_loss: 0.2376 - val_accuracy: 0.9286\n",
      "Epoch 5/10\n",
      "6134/6134 [==============================] - 265s 43ms/step - loss: 0.2869 - accuracy: 0.9160 - val_loss: 0.3030 - val_accuracy: 0.9173\n",
      "Epoch 6/10\n",
      "6134/6134 [==============================] - 318s 52ms/step - loss: 0.2584 - accuracy: 0.9253 - val_loss: 0.3004 - val_accuracy: 0.9173\n",
      "Epoch 7/10\n",
      "6134/6134 [==============================] - 273s 45ms/step - loss: 0.2353 - accuracy: 0.9314 - val_loss: 0.2622 - val_accuracy: 0.9236\n",
      "Epoch 8/10\n",
      "6134/6134 [==============================] - 281s 46ms/step - loss: 0.2205 - accuracy: 0.9361 - val_loss: 1.6370 - val_accuracy: 0.8706\n",
      "Epoch 9/10\n",
      "6134/6134 [==============================] - 271s 44ms/step - loss: 0.2099 - accuracy: 0.9391 - val_loss: 0.4888 - val_accuracy: 0.9113\n",
      "Epoch 10/10\n",
      "6134/6134 [==============================] - 280s 46ms/step - loss: 0.1972 - accuracy: 0.9435 - val_loss: 0.2537 - val_accuracy: 0.9267\n",
      "Model: \"model_4\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_6 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_188 (Conv2D)             (None, 149, 149, 32) 864         input_6[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_192 (BatchN (None, 149, 149, 32) 96          conv2d_188[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_192 (Activation)     (None, 149, 149, 32) 0           batch_normalization_192[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_189 (Conv2D)             (None, 147, 147, 32) 9216        activation_192[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_193 (BatchN (None, 147, 147, 32) 96          conv2d_189[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_193 (Activation)     (None, 147, 147, 32) 0           batch_normalization_193[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_190 (Conv2D)             (None, 147, 147, 64) 18432       activation_193[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_194 (BatchN (None, 147, 147, 64) 192         conv2d_190[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_194 (Activation)     (None, 147, 147, 64) 0           batch_normalization_194[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2D)  (None, 73, 73, 64)   0           activation_194[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_191 (Conv2D)             (None, 73, 73, 80)   5120        max_pooling2d_8[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_195 (BatchN (None, 73, 73, 80)   240         conv2d_191[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_195 (Activation)     (None, 73, 73, 80)   0           batch_normalization_195[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_192 (Conv2D)             (None, 71, 71, 192)  138240      activation_195[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_196 (BatchN (None, 71, 71, 192)  576         conv2d_192[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_196 (Activation)     (None, 71, 71, 192)  0           batch_normalization_196[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2D)  (None, 35, 35, 192)  0           activation_196[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_196 (Conv2D)             (None, 35, 35, 64)   12288       max_pooling2d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_200 (BatchN (None, 35, 35, 64)   192         conv2d_196[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_200 (Activation)     (None, 35, 35, 64)   0           batch_normalization_200[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_194 (Conv2D)             (None, 35, 35, 48)   9216        max_pooling2d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_197 (Conv2D)             (None, 35, 35, 96)   55296       activation_200[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_198 (BatchN (None, 35, 35, 48)   144         conv2d_194[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_201 (BatchN (None, 35, 35, 96)   288         conv2d_197[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_198 (Activation)     (None, 35, 35, 48)   0           batch_normalization_198[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_201 (Activation)     (None, 35, 35, 96)   0           batch_normalization_201[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_18 (AveragePo (None, 35, 35, 192)  0           max_pooling2d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_193 (Conv2D)             (None, 35, 35, 64)   12288       max_pooling2d_9[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_195 (Conv2D)             (None, 35, 35, 64)   76800       activation_198[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_198 (Conv2D)             (None, 35, 35, 96)   82944       activation_201[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_199 (Conv2D)             (None, 35, 35, 32)   6144        average_pooling2d_18[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_197 (BatchN (None, 35, 35, 64)   192         conv2d_193[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_199 (BatchN (None, 35, 35, 64)   192         conv2d_195[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_202 (BatchN (None, 35, 35, 96)   288         conv2d_198[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_203 (BatchN (None, 35, 35, 32)   96          conv2d_199[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_197 (Activation)     (None, 35, 35, 64)   0           batch_normalization_197[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_199 (Activation)     (None, 35, 35, 64)   0           batch_normalization_199[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_202 (Activation)     (None, 35, 35, 96)   0           batch_normalization_202[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_203 (Activation)     (None, 35, 35, 32)   0           batch_normalization_203[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 35, 35, 256)  0           activation_197[0][0]             \n",
      "                                                                 activation_199[0][0]             \n",
      "                                                                 activation_202[0][0]             \n",
      "                                                                 activation_203[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_203 (Conv2D)             (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_207 (BatchN (None, 35, 35, 64)   192         conv2d_203[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_207 (Activation)     (None, 35, 35, 64)   0           batch_normalization_207[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_201 (Conv2D)             (None, 35, 35, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_204 (Conv2D)             (None, 35, 35, 96)   55296       activation_207[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_205 (BatchN (None, 35, 35, 48)   144         conv2d_201[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_208 (BatchN (None, 35, 35, 96)   288         conv2d_204[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_205 (Activation)     (None, 35, 35, 48)   0           batch_normalization_205[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_208 (Activation)     (None, 35, 35, 96)   0           batch_normalization_208[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_19 (AveragePo (None, 35, 35, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_200 (Conv2D)             (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_202 (Conv2D)             (None, 35, 35, 64)   76800       activation_205[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_205 (Conv2D)             (None, 35, 35, 96)   82944       activation_208[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_206 (Conv2D)             (None, 35, 35, 64)   16384       average_pooling2d_19[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_204 (BatchN (None, 35, 35, 64)   192         conv2d_200[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_206 (BatchN (None, 35, 35, 64)   192         conv2d_202[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_209 (BatchN (None, 35, 35, 96)   288         conv2d_205[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_210 (BatchN (None, 35, 35, 64)   192         conv2d_206[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_204 (Activation)     (None, 35, 35, 64)   0           batch_normalization_204[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_206 (Activation)     (None, 35, 35, 64)   0           batch_normalization_206[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_209 (Activation)     (None, 35, 35, 96)   0           batch_normalization_209[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_210 (Activation)     (None, 35, 35, 64)   0           batch_normalization_210[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 35, 35, 288)  0           activation_204[0][0]             \n",
      "                                                                 activation_206[0][0]             \n",
      "                                                                 activation_209[0][0]             \n",
      "                                                                 activation_210[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_210 (Conv2D)             (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_214 (BatchN (None, 35, 35, 64)   192         conv2d_210[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_214 (Activation)     (None, 35, 35, 64)   0           batch_normalization_214[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_208 (Conv2D)             (None, 35, 35, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_211 (Conv2D)             (None, 35, 35, 96)   55296       activation_214[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_212 (BatchN (None, 35, 35, 48)   144         conv2d_208[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_215 (BatchN (None, 35, 35, 96)   288         conv2d_211[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_212 (Activation)     (None, 35, 35, 48)   0           batch_normalization_212[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_215 (Activation)     (None, 35, 35, 96)   0           batch_normalization_215[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_20 (AveragePo (None, 35, 35, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_207 (Conv2D)             (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_209 (Conv2D)             (None, 35, 35, 64)   76800       activation_212[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_212 (Conv2D)             (None, 35, 35, 96)   82944       activation_215[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_213 (Conv2D)             (None, 35, 35, 64)   18432       average_pooling2d_20[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_211 (BatchN (None, 35, 35, 64)   192         conv2d_207[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_213 (BatchN (None, 35, 35, 64)   192         conv2d_209[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_216 (BatchN (None, 35, 35, 96)   288         conv2d_212[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_217 (BatchN (None, 35, 35, 64)   192         conv2d_213[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_211 (Activation)     (None, 35, 35, 64)   0           batch_normalization_211[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_213 (Activation)     (None, 35, 35, 64)   0           batch_normalization_213[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_216 (Activation)     (None, 35, 35, 96)   0           batch_normalization_216[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_217 (Activation)     (None, 35, 35, 64)   0           batch_normalization_217[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 35, 35, 288)  0           activation_211[0][0]             \n",
      "                                                                 activation_213[0][0]             \n",
      "                                                                 activation_216[0][0]             \n",
      "                                                                 activation_217[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_215 (Conv2D)             (None, 35, 35, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_219 (BatchN (None, 35, 35, 64)   192         conv2d_215[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_219 (Activation)     (None, 35, 35, 64)   0           batch_normalization_219[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_216 (Conv2D)             (None, 35, 35, 96)   55296       activation_219[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_220 (BatchN (None, 35, 35, 96)   288         conv2d_216[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_220 (Activation)     (None, 35, 35, 96)   0           batch_normalization_220[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_214 (Conv2D)             (None, 17, 17, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_217 (Conv2D)             (None, 17, 17, 96)   82944       activation_220[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_218 (BatchN (None, 17, 17, 384)  1152        conv2d_214[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_221 (BatchN (None, 17, 17, 96)   288         conv2d_217[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_218 (Activation)     (None, 17, 17, 384)  0           batch_normalization_218[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_221 (Activation)     (None, 17, 17, 96)   0           batch_normalization_221[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling2D) (None, 17, 17, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 17, 17, 768)  0           activation_218[0][0]             \n",
      "                                                                 activation_221[0][0]             \n",
      "                                                                 max_pooling2d_10[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_222 (Conv2D)             (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_226 (BatchN (None, 17, 17, 128)  384         conv2d_222[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_226 (Activation)     (None, 17, 17, 128)  0           batch_normalization_226[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_223 (Conv2D)             (None, 17, 17, 128)  114688      activation_226[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_227 (BatchN (None, 17, 17, 128)  384         conv2d_223[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_227 (Activation)     (None, 17, 17, 128)  0           batch_normalization_227[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_219 (Conv2D)             (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_224 (Conv2D)             (None, 17, 17, 128)  114688      activation_227[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_223 (BatchN (None, 17, 17, 128)  384         conv2d_219[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_228 (BatchN (None, 17, 17, 128)  384         conv2d_224[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_223 (Activation)     (None, 17, 17, 128)  0           batch_normalization_223[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_228 (Activation)     (None, 17, 17, 128)  0           batch_normalization_228[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_220 (Conv2D)             (None, 17, 17, 128)  114688      activation_223[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_225 (Conv2D)             (None, 17, 17, 128)  114688      activation_228[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_224 (BatchN (None, 17, 17, 128)  384         conv2d_220[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_229 (BatchN (None, 17, 17, 128)  384         conv2d_225[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_224 (Activation)     (None, 17, 17, 128)  0           batch_normalization_224[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_229 (Activation)     (None, 17, 17, 128)  0           batch_normalization_229[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_21 (AveragePo (None, 17, 17, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_218 (Conv2D)             (None, 17, 17, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_221 (Conv2D)             (None, 17, 17, 192)  172032      activation_224[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_226 (Conv2D)             (None, 17, 17, 192)  172032      activation_229[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_227 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_21[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_222 (BatchN (None, 17, 17, 192)  576         conv2d_218[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_225 (BatchN (None, 17, 17, 192)  576         conv2d_221[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_230 (BatchN (None, 17, 17, 192)  576         conv2d_226[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_231 (BatchN (None, 17, 17, 192)  576         conv2d_227[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_222 (Activation)     (None, 17, 17, 192)  0           batch_normalization_222[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_225 (Activation)     (None, 17, 17, 192)  0           batch_normalization_225[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_230 (Activation)     (None, 17, 17, 192)  0           batch_normalization_230[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_231 (Activation)     (None, 17, 17, 192)  0           batch_normalization_231[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 17, 17, 768)  0           activation_222[0][0]             \n",
      "                                                                 activation_225[0][0]             \n",
      "                                                                 activation_230[0][0]             \n",
      "                                                                 activation_231[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_232 (Conv2D)             (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_236 (BatchN (None, 17, 17, 160)  480         conv2d_232[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_236 (Activation)     (None, 17, 17, 160)  0           batch_normalization_236[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_233 (Conv2D)             (None, 17, 17, 160)  179200      activation_236[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_237 (BatchN (None, 17, 17, 160)  480         conv2d_233[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_237 (Activation)     (None, 17, 17, 160)  0           batch_normalization_237[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_229 (Conv2D)             (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_234 (Conv2D)             (None, 17, 17, 160)  179200      activation_237[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_233 (BatchN (None, 17, 17, 160)  480         conv2d_229[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_238 (BatchN (None, 17, 17, 160)  480         conv2d_234[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_233 (Activation)     (None, 17, 17, 160)  0           batch_normalization_233[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_238 (Activation)     (None, 17, 17, 160)  0           batch_normalization_238[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_230 (Conv2D)             (None, 17, 17, 160)  179200      activation_233[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_235 (Conv2D)             (None, 17, 17, 160)  179200      activation_238[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_234 (BatchN (None, 17, 17, 160)  480         conv2d_230[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_239 (BatchN (None, 17, 17, 160)  480         conv2d_235[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_234 (Activation)     (None, 17, 17, 160)  0           batch_normalization_234[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_239 (Activation)     (None, 17, 17, 160)  0           batch_normalization_239[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_22 (AveragePo (None, 17, 17, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_228 (Conv2D)             (None, 17, 17, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_231 (Conv2D)             (None, 17, 17, 192)  215040      activation_234[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_236 (Conv2D)             (None, 17, 17, 192)  215040      activation_239[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_237 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_22[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_232 (BatchN (None, 17, 17, 192)  576         conv2d_228[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_235 (BatchN (None, 17, 17, 192)  576         conv2d_231[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_240 (BatchN (None, 17, 17, 192)  576         conv2d_236[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_241 (BatchN (None, 17, 17, 192)  576         conv2d_237[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_232 (Activation)     (None, 17, 17, 192)  0           batch_normalization_232[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_235 (Activation)     (None, 17, 17, 192)  0           batch_normalization_235[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_240 (Activation)     (None, 17, 17, 192)  0           batch_normalization_240[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_241 (Activation)     (None, 17, 17, 192)  0           batch_normalization_241[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 17, 17, 768)  0           activation_232[0][0]             \n",
      "                                                                 activation_235[0][0]             \n",
      "                                                                 activation_240[0][0]             \n",
      "                                                                 activation_241[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_242 (Conv2D)             (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_246 (BatchN (None, 17, 17, 160)  480         conv2d_242[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_246 (Activation)     (None, 17, 17, 160)  0           batch_normalization_246[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_243 (Conv2D)             (None, 17, 17, 160)  179200      activation_246[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_247 (BatchN (None, 17, 17, 160)  480         conv2d_243[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_247 (Activation)     (None, 17, 17, 160)  0           batch_normalization_247[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_239 (Conv2D)             (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_244 (Conv2D)             (None, 17, 17, 160)  179200      activation_247[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_243 (BatchN (None, 17, 17, 160)  480         conv2d_239[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_248 (BatchN (None, 17, 17, 160)  480         conv2d_244[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_243 (Activation)     (None, 17, 17, 160)  0           batch_normalization_243[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_248 (Activation)     (None, 17, 17, 160)  0           batch_normalization_248[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_240 (Conv2D)             (None, 17, 17, 160)  179200      activation_243[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_245 (Conv2D)             (None, 17, 17, 160)  179200      activation_248[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_244 (BatchN (None, 17, 17, 160)  480         conv2d_240[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_249 (BatchN (None, 17, 17, 160)  480         conv2d_245[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_244 (Activation)     (None, 17, 17, 160)  0           batch_normalization_244[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_249 (Activation)     (None, 17, 17, 160)  0           batch_normalization_249[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_23 (AveragePo (None, 17, 17, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_238 (Conv2D)             (None, 17, 17, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_241 (Conv2D)             (None, 17, 17, 192)  215040      activation_244[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_246 (Conv2D)             (None, 17, 17, 192)  215040      activation_249[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_247 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_23[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_242 (BatchN (None, 17, 17, 192)  576         conv2d_238[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_245 (BatchN (None, 17, 17, 192)  576         conv2d_241[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_250 (BatchN (None, 17, 17, 192)  576         conv2d_246[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_251 (BatchN (None, 17, 17, 192)  576         conv2d_247[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_242 (Activation)     (None, 17, 17, 192)  0           batch_normalization_242[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_245 (Activation)     (None, 17, 17, 192)  0           batch_normalization_245[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_250 (Activation)     (None, 17, 17, 192)  0           batch_normalization_250[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_251 (Activation)     (None, 17, 17, 192)  0           batch_normalization_251[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 17, 17, 768)  0           activation_242[0][0]             \n",
      "                                                                 activation_245[0][0]             \n",
      "                                                                 activation_250[0][0]             \n",
      "                                                                 activation_251[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_252 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_256 (BatchN (None, 17, 17, 192)  576         conv2d_252[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_256 (Activation)     (None, 17, 17, 192)  0           batch_normalization_256[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_253 (Conv2D)             (None, 17, 17, 192)  258048      activation_256[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_257 (BatchN (None, 17, 17, 192)  576         conv2d_253[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_257 (Activation)     (None, 17, 17, 192)  0           batch_normalization_257[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_249 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_254 (Conv2D)             (None, 17, 17, 192)  258048      activation_257[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_253 (BatchN (None, 17, 17, 192)  576         conv2d_249[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_258 (BatchN (None, 17, 17, 192)  576         conv2d_254[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_253 (Activation)     (None, 17, 17, 192)  0           batch_normalization_253[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_258 (Activation)     (None, 17, 17, 192)  0           batch_normalization_258[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_250 (Conv2D)             (None, 17, 17, 192)  258048      activation_253[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_255 (Conv2D)             (None, 17, 17, 192)  258048      activation_258[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_254 (BatchN (None, 17, 17, 192)  576         conv2d_250[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_259 (BatchN (None, 17, 17, 192)  576         conv2d_255[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_254 (Activation)     (None, 17, 17, 192)  0           batch_normalization_254[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_259 (Activation)     (None, 17, 17, 192)  0           batch_normalization_259[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_24 (AveragePo (None, 17, 17, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_248 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_251 (Conv2D)             (None, 17, 17, 192)  258048      activation_254[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_256 (Conv2D)             (None, 17, 17, 192)  258048      activation_259[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_257 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_24[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_252 (BatchN (None, 17, 17, 192)  576         conv2d_248[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_255 (BatchN (None, 17, 17, 192)  576         conv2d_251[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_260 (BatchN (None, 17, 17, 192)  576         conv2d_256[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_261 (BatchN (None, 17, 17, 192)  576         conv2d_257[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_252 (Activation)     (None, 17, 17, 192)  0           batch_normalization_252[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_255 (Activation)     (None, 17, 17, 192)  0           batch_normalization_255[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_260 (Activation)     (None, 17, 17, 192)  0           batch_normalization_260[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_261 (Activation)     (None, 17, 17, 192)  0           batch_normalization_261[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 17, 17, 768)  0           activation_252[0][0]             \n",
      "                                                                 activation_255[0][0]             \n",
      "                                                                 activation_260[0][0]             \n",
      "                                                                 activation_261[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_260 (Conv2D)             (None, 17, 17, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_264 (BatchN (None, 17, 17, 192)  576         conv2d_260[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_264 (Activation)     (None, 17, 17, 192)  0           batch_normalization_264[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_261 (Conv2D)             (None, 17, 17, 192)  258048      activation_264[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_265 (BatchN (None, 17, 17, 192)  576         conv2d_261[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_265 (Activation)     (None, 17, 17, 192)  0           batch_normalization_265[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_258 (Conv2D)             (None, 17, 17, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_262 (Conv2D)             (None, 17, 17, 192)  258048      activation_265[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_262 (BatchN (None, 17, 17, 192)  576         conv2d_258[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_266 (BatchN (None, 17, 17, 192)  576         conv2d_262[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_262 (Activation)     (None, 17, 17, 192)  0           batch_normalization_262[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_266 (Activation)     (None, 17, 17, 192)  0           batch_normalization_266[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_259 (Conv2D)             (None, 8, 8, 320)    552960      activation_262[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_263 (Conv2D)             (None, 8, 8, 192)    331776      activation_266[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_263 (BatchN (None, 8, 8, 320)    960         conv2d_259[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_267 (BatchN (None, 8, 8, 192)    576         conv2d_263[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_263 (Activation)     (None, 8, 8, 320)    0           batch_normalization_263[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_267 (Activation)     (None, 8, 8, 192)    0           batch_normalization_267[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_11 (MaxPooling2D) (None, 8, 8, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 8, 8, 1280)   0           activation_263[0][0]             \n",
      "                                                                 activation_267[0][0]             \n",
      "                                                                 max_pooling2d_11[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_268 (Conv2D)             (None, 8, 8, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_272 (BatchN (None, 8, 8, 448)    1344        conv2d_268[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_272 (Activation)     (None, 8, 8, 448)    0           batch_normalization_272[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_265 (Conv2D)             (None, 8, 8, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_269 (Conv2D)             (None, 8, 8, 384)    1548288     activation_272[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_269 (BatchN (None, 8, 8, 384)    1152        conv2d_265[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_273 (BatchN (None, 8, 8, 384)    1152        conv2d_269[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_269 (Activation)     (None, 8, 8, 384)    0           batch_normalization_269[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_273 (Activation)     (None, 8, 8, 384)    0           batch_normalization_273[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_266 (Conv2D)             (None, 8, 8, 384)    442368      activation_269[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_267 (Conv2D)             (None, 8, 8, 384)    442368      activation_269[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_270 (Conv2D)             (None, 8, 8, 384)    442368      activation_273[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_271 (Conv2D)             (None, 8, 8, 384)    442368      activation_273[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_25 (AveragePo (None, 8, 8, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_264 (Conv2D)             (None, 8, 8, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_270 (BatchN (None, 8, 8, 384)    1152        conv2d_266[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_271 (BatchN (None, 8, 8, 384)    1152        conv2d_267[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_274 (BatchN (None, 8, 8, 384)    1152        conv2d_270[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_275 (BatchN (None, 8, 8, 384)    1152        conv2d_271[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_272 (Conv2D)             (None, 8, 8, 192)    245760      average_pooling2d_25[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_268 (BatchN (None, 8, 8, 320)    960         conv2d_264[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_270 (Activation)     (None, 8, 8, 384)    0           batch_normalization_270[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_271 (Activation)     (None, 8, 8, 384)    0           batch_normalization_271[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_274 (Activation)     (None, 8, 8, 384)    0           batch_normalization_274[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_275 (Activation)     (None, 8, 8, 384)    0           batch_normalization_275[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_276 (BatchN (None, 8, 8, 192)    576         conv2d_272[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_268 (Activation)     (None, 8, 8, 320)    0           batch_normalization_268[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 8, 8, 768)    0           activation_270[0][0]             \n",
      "                                                                 activation_271[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 8, 8, 768)    0           activation_274[0][0]             \n",
      "                                                                 activation_275[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_276 (Activation)     (None, 8, 8, 192)    0           batch_normalization_276[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 8, 8, 2048)   0           activation_268[0][0]             \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_4[0][0]              \n",
      "                                                                 activation_276[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_277 (Conv2D)             (None, 8, 8, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_281 (BatchN (None, 8, 8, 448)    1344        conv2d_277[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_281 (Activation)     (None, 8, 8, 448)    0           batch_normalization_281[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_274 (Conv2D)             (None, 8, 8, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_278 (Conv2D)             (None, 8, 8, 384)    1548288     activation_281[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_278 (BatchN (None, 8, 8, 384)    1152        conv2d_274[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_282 (BatchN (None, 8, 8, 384)    1152        conv2d_278[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_278 (Activation)     (None, 8, 8, 384)    0           batch_normalization_278[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_282 (Activation)     (None, 8, 8, 384)    0           batch_normalization_282[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_275 (Conv2D)             (None, 8, 8, 384)    442368      activation_278[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_276 (Conv2D)             (None, 8, 8, 384)    442368      activation_278[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_279 (Conv2D)             (None, 8, 8, 384)    442368      activation_282[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_280 (Conv2D)             (None, 8, 8, 384)    442368      activation_282[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_26 (AveragePo (None, 8, 8, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_273 (Conv2D)             (None, 8, 8, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_279 (BatchN (None, 8, 8, 384)    1152        conv2d_275[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_280 (BatchN (None, 8, 8, 384)    1152        conv2d_276[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_283 (BatchN (None, 8, 8, 384)    1152        conv2d_279[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_284 (BatchN (None, 8, 8, 384)    1152        conv2d_280[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_281 (Conv2D)             (None, 8, 8, 192)    393216      average_pooling2d_26[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_277 (BatchN (None, 8, 8, 320)    960         conv2d_273[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_279 (Activation)     (None, 8, 8, 384)    0           batch_normalization_279[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_280 (Activation)     (None, 8, 8, 384)    0           batch_normalization_280[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_283 (Activation)     (None, 8, 8, 384)    0           batch_normalization_283[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_284 (Activation)     (None, 8, 8, 384)    0           batch_normalization_284[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_285 (BatchN (None, 8, 8, 192)    576         conv2d_281[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_277 (Activation)     (None, 8, 8, 320)    0           batch_normalization_277[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 8, 8, 768)    0           activation_279[0][0]             \n",
      "                                                                 activation_280[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_5 (Concatenate)     (None, 8, 8, 768)    0           activation_283[0][0]             \n",
      "                                                                 activation_284[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_285 (Activation)     (None, 8, 8, 192)    0           batch_normalization_285[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 8, 8, 2048)   0           activation_277[0][0]             \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_5[0][0]              \n",
      "                                                                 activation_285[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_7 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, 1, 1, 2048)   0           global_average_pooling2d_7[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dense_14 (Dense)                (None, 1, 1, 128)    262144      reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_15 (Dense)                (None, 1, 1, 2048)   262144      dense_14[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_1 (Multiply)           (None, 8, 8, 2048)   0           mixed10[0][0]                    \n",
      "                                                                 dense_15[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_8 (Glo (None, 2048)         0           multiply_1[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_16 (Dense)                (None, 1024)         2098176     global_average_pooling2d_8[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dense_17 (Dense)                (None, 7)            7175        dense_16[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 24,432,423\n",
      "Trainable params: 24,397,991\n",
      "Non-trainable params: 34,432\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.316213900Z",
     "start_time": "2024-12-15T07:25:36.101285Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "a1c23203d47fd400",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.317211200Z",
     "start_time": "2024-12-15T07:26:27.899177Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "f01432ea946b03df",
   "outputs": [],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.317211200Z",
     "start_time": "2024-12-15T07:48:49.441264Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from tensorflow.keras.applications import InceptionV3\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Flatten, Reshape, multiply, Lambda\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "def build_attention_model(input_shape, num_classes):\n",
    "    # 使用InceptionV3作为基础模型\n",
    "    base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "    \n",
    "    # 全局平均池化\n",
    "    x = GlobalAveragePooling2D()(base_model.output)\n",
    "    \n",
    "    # 添加全连接层\n",
    "    x = Dense(1024, activation='relu')(x)\n",
    "    \n",
    "    # 构建注意力机制\n",
    "    attention = Dense(1, activation='tanh')(x)\n",
    "    attention = Reshape((-1, 1, 1))(attention)\n",
    "    attention = multiply([x, attention])\n",
    "    attention = Lambda(lambda x: K.sum(x, axis=-2))(attention)\n",
    "    attention = Lambda(lambda x: K.expand_dims(x, axis=-1))(attention)\n",
    "    attention = GlobalAveragePooling2D()(attention)\n",
    "    \n",
    "    # 输出层\n",
    "    predictions = Dense(num_classes, activation='softmax')(attention)\n",
    "    \n",
    "    # 构建模型\n",
    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
    "    \n",
    "    # 冻结基础模型的层\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "    \n",
    "    return model\n",
    "\n",
    "# 定义输入形状和类别数\n",
    "input_shape = (299, 299, 3)  # InceptionV3的输入形状\n",
    "num_classes = len(train_generator.class_indices)  # 类别数\n",
    "\n",
    "# 构建注意力模型\n",
    "attention_model = build_attention_model(input_shape, num_classes)\n",
    "attention_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# 训练注意力模型\n",
    "print(\"Training Attention model...\")\n",
    "history_attention = attention_model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    epochs=20\n",
    ")"
   ],
   "id": "7782483ebf7b983e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Attention model...\n",
      "Epoch 1/10\n",
      "6134/6134 [==============================] - 332s 54ms/step - loss: 1.4351 - accuracy: 0.4300 - val_loss: 1.5440 - val_accuracy: 0.3709\n",
      "Epoch 2/10\n",
      "6134/6134 [==============================] - 181s 30ms/step - loss: 1.2609 - accuracy: 0.4900 - val_loss: 1.6026 - val_accuracy: 0.3904\n",
      "Epoch 3/10\n",
      "6134/6134 [==============================] - 181s 30ms/step - loss: 1.1947 - accuracy: 0.5257 - val_loss: 1.3760 - val_accuracy: 0.4461\n",
      "Epoch 4/10\n",
      "6134/6134 [==============================] - 182s 30ms/step - loss: 1.1689 - accuracy: 0.5474 - val_loss: 1.3215 - val_accuracy: 0.4768\n",
      "Epoch 5/10\n",
      "6134/6134 [==============================] - 193s 31ms/step - loss: 1.1418 - accuracy: 0.5520 - val_loss: 1.2815 - val_accuracy: 0.5022\n",
      "Epoch 6/10\n",
      "6134/6134 [==============================] - 194s 32ms/step - loss: 1.1750 - accuracy: 0.5645 - val_loss: 1.2709 - val_accuracy: 0.4900\n",
      "Epoch 7/10\n",
      "6134/6134 [==============================] - 189s 31ms/step - loss: 1.1238 - accuracy: 0.5782 - val_loss: 1.3020 - val_accuracy: 0.5338\n",
      "Epoch 8/10\n",
      "6134/6134 [==============================] - 312s 51ms/step - loss: 1.1160 - accuracy: 0.5924 - val_loss: 1.2605 - val_accuracy: 0.5232\n",
      "Epoch 9/10\n",
      "6134/6134 [==============================] - 188s 31ms/step - loss: 1.0995 - accuracy: 0.5829 - val_loss: 1.2170 - val_accuracy: 0.5316\n",
      "Epoch 10/10\n",
      "6134/6134 [==============================] - 186s 30ms/step - loss: 1.0768 - accuracy: 0.5907 - val_loss: 1.2336 - val_accuracy: 0.5310\n"
     ]
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.317211200Z",
     "start_time": "2024-12-15T08:24:38.748751Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# 打印模型摘要\n",
    "attention_model.summary()"
   ],
   "id": "7032282d6a04331f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_5\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_7 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_282 (Conv2D)             (None, 149, 149, 32) 864         input_7[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_286 (BatchN (None, 149, 149, 32) 96          conv2d_282[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_286 (Activation)     (None, 149, 149, 32) 0           batch_normalization_286[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_283 (Conv2D)             (None, 147, 147, 32) 9216        activation_286[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_287 (BatchN (None, 147, 147, 32) 96          conv2d_283[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_287 (Activation)     (None, 147, 147, 32) 0           batch_normalization_287[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_284 (Conv2D)             (None, 147, 147, 64) 18432       activation_287[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_288 (BatchN (None, 147, 147, 64) 192         conv2d_284[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_288 (Activation)     (None, 147, 147, 64) 0           batch_normalization_288[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_12 (MaxPooling2D) (None, 73, 73, 64)   0           activation_288[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_285 (Conv2D)             (None, 73, 73, 80)   5120        max_pooling2d_12[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_289 (BatchN (None, 73, 73, 80)   240         conv2d_285[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_289 (Activation)     (None, 73, 73, 80)   0           batch_normalization_289[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_286 (Conv2D)             (None, 71, 71, 192)  138240      activation_289[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_290 (BatchN (None, 71, 71, 192)  576         conv2d_286[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_290 (Activation)     (None, 71, 71, 192)  0           batch_normalization_290[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_13 (MaxPooling2D) (None, 35, 35, 192)  0           activation_290[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_290 (Conv2D)             (None, 35, 35, 64)   12288       max_pooling2d_13[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_294 (BatchN (None, 35, 35, 64)   192         conv2d_290[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_294 (Activation)     (None, 35, 35, 64)   0           batch_normalization_294[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_288 (Conv2D)             (None, 35, 35, 48)   9216        max_pooling2d_13[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_291 (Conv2D)             (None, 35, 35, 96)   55296       activation_294[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_292 (BatchN (None, 35, 35, 48)   144         conv2d_288[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_295 (BatchN (None, 35, 35, 96)   288         conv2d_291[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_292 (Activation)     (None, 35, 35, 48)   0           batch_normalization_292[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_295 (Activation)     (None, 35, 35, 96)   0           batch_normalization_295[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_27 (AveragePo (None, 35, 35, 192)  0           max_pooling2d_13[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_287 (Conv2D)             (None, 35, 35, 64)   12288       max_pooling2d_13[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_289 (Conv2D)             (None, 35, 35, 64)   76800       activation_292[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_292 (Conv2D)             (None, 35, 35, 96)   82944       activation_295[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_293 (Conv2D)             (None, 35, 35, 32)   6144        average_pooling2d_27[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_291 (BatchN (None, 35, 35, 64)   192         conv2d_287[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_293 (BatchN (None, 35, 35, 64)   192         conv2d_289[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_296 (BatchN (None, 35, 35, 96)   288         conv2d_292[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_297 (BatchN (None, 35, 35, 32)   96          conv2d_293[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_291 (Activation)     (None, 35, 35, 64)   0           batch_normalization_291[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_293 (Activation)     (None, 35, 35, 64)   0           batch_normalization_293[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_296 (Activation)     (None, 35, 35, 96)   0           batch_normalization_296[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_297 (Activation)     (None, 35, 35, 32)   0           batch_normalization_297[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 35, 35, 256)  0           activation_291[0][0]             \n",
      "                                                                 activation_293[0][0]             \n",
      "                                                                 activation_296[0][0]             \n",
      "                                                                 activation_297[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_297 (Conv2D)             (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_301 (BatchN (None, 35, 35, 64)   192         conv2d_297[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_301 (Activation)     (None, 35, 35, 64)   0           batch_normalization_301[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_295 (Conv2D)             (None, 35, 35, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_298 (Conv2D)             (None, 35, 35, 96)   55296       activation_301[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_299 (BatchN (None, 35, 35, 48)   144         conv2d_295[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_302 (BatchN (None, 35, 35, 96)   288         conv2d_298[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_299 (Activation)     (None, 35, 35, 48)   0           batch_normalization_299[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_302 (Activation)     (None, 35, 35, 96)   0           batch_normalization_302[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_28 (AveragePo (None, 35, 35, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_294 (Conv2D)             (None, 35, 35, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_296 (Conv2D)             (None, 35, 35, 64)   76800       activation_299[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_299 (Conv2D)             (None, 35, 35, 96)   82944       activation_302[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_300 (Conv2D)             (None, 35, 35, 64)   16384       average_pooling2d_28[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_298 (BatchN (None, 35, 35, 64)   192         conv2d_294[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_300 (BatchN (None, 35, 35, 64)   192         conv2d_296[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_303 (BatchN (None, 35, 35, 96)   288         conv2d_299[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_304 (BatchN (None, 35, 35, 64)   192         conv2d_300[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_298 (Activation)     (None, 35, 35, 64)   0           batch_normalization_298[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_300 (Activation)     (None, 35, 35, 64)   0           batch_normalization_300[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_303 (Activation)     (None, 35, 35, 96)   0           batch_normalization_303[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_304 (Activation)     (None, 35, 35, 64)   0           batch_normalization_304[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 35, 35, 288)  0           activation_298[0][0]             \n",
      "                                                                 activation_300[0][0]             \n",
      "                                                                 activation_303[0][0]             \n",
      "                                                                 activation_304[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_304 (Conv2D)             (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_308 (BatchN (None, 35, 35, 64)   192         conv2d_304[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_308 (Activation)     (None, 35, 35, 64)   0           batch_normalization_308[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_302 (Conv2D)             (None, 35, 35, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_305 (Conv2D)             (None, 35, 35, 96)   55296       activation_308[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_306 (BatchN (None, 35, 35, 48)   144         conv2d_302[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_309 (BatchN (None, 35, 35, 96)   288         conv2d_305[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_306 (Activation)     (None, 35, 35, 48)   0           batch_normalization_306[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_309 (Activation)     (None, 35, 35, 96)   0           batch_normalization_309[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_29 (AveragePo (None, 35, 35, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_301 (Conv2D)             (None, 35, 35, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_303 (Conv2D)             (None, 35, 35, 64)   76800       activation_306[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_306 (Conv2D)             (None, 35, 35, 96)   82944       activation_309[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_307 (Conv2D)             (None, 35, 35, 64)   18432       average_pooling2d_29[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_305 (BatchN (None, 35, 35, 64)   192         conv2d_301[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_307 (BatchN (None, 35, 35, 64)   192         conv2d_303[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_310 (BatchN (None, 35, 35, 96)   288         conv2d_306[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_311 (BatchN (None, 35, 35, 64)   192         conv2d_307[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_305 (Activation)     (None, 35, 35, 64)   0           batch_normalization_305[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_307 (Activation)     (None, 35, 35, 64)   0           batch_normalization_307[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_310 (Activation)     (None, 35, 35, 96)   0           batch_normalization_310[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_311 (Activation)     (None, 35, 35, 64)   0           batch_normalization_311[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 35, 35, 288)  0           activation_305[0][0]             \n",
      "                                                                 activation_307[0][0]             \n",
      "                                                                 activation_310[0][0]             \n",
      "                                                                 activation_311[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_309 (Conv2D)             (None, 35, 35, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_313 (BatchN (None, 35, 35, 64)   192         conv2d_309[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_313 (Activation)     (None, 35, 35, 64)   0           batch_normalization_313[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_310 (Conv2D)             (None, 35, 35, 96)   55296       activation_313[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_314 (BatchN (None, 35, 35, 96)   288         conv2d_310[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_314 (Activation)     (None, 35, 35, 96)   0           batch_normalization_314[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_308 (Conv2D)             (None, 17, 17, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_311 (Conv2D)             (None, 17, 17, 96)   82944       activation_314[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_312 (BatchN (None, 17, 17, 384)  1152        conv2d_308[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_315 (BatchN (None, 17, 17, 96)   288         conv2d_311[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_312 (Activation)     (None, 17, 17, 384)  0           batch_normalization_312[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_315 (Activation)     (None, 17, 17, 96)   0           batch_normalization_315[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_14 (MaxPooling2D) (None, 17, 17, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 17, 17, 768)  0           activation_312[0][0]             \n",
      "                                                                 activation_315[0][0]             \n",
      "                                                                 max_pooling2d_14[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_316 (Conv2D)             (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_320 (BatchN (None, 17, 17, 128)  384         conv2d_316[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_320 (Activation)     (None, 17, 17, 128)  0           batch_normalization_320[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_317 (Conv2D)             (None, 17, 17, 128)  114688      activation_320[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_321 (BatchN (None, 17, 17, 128)  384         conv2d_317[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_321 (Activation)     (None, 17, 17, 128)  0           batch_normalization_321[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_313 (Conv2D)             (None, 17, 17, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_318 (Conv2D)             (None, 17, 17, 128)  114688      activation_321[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_317 (BatchN (None, 17, 17, 128)  384         conv2d_313[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_322 (BatchN (None, 17, 17, 128)  384         conv2d_318[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_317 (Activation)     (None, 17, 17, 128)  0           batch_normalization_317[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_322 (Activation)     (None, 17, 17, 128)  0           batch_normalization_322[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_314 (Conv2D)             (None, 17, 17, 128)  114688      activation_317[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_319 (Conv2D)             (None, 17, 17, 128)  114688      activation_322[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_318 (BatchN (None, 17, 17, 128)  384         conv2d_314[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_323 (BatchN (None, 17, 17, 128)  384         conv2d_319[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_318 (Activation)     (None, 17, 17, 128)  0           batch_normalization_318[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_323 (Activation)     (None, 17, 17, 128)  0           batch_normalization_323[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_30 (AveragePo (None, 17, 17, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_312 (Conv2D)             (None, 17, 17, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_315 (Conv2D)             (None, 17, 17, 192)  172032      activation_318[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_320 (Conv2D)             (None, 17, 17, 192)  172032      activation_323[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_321 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_30[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_316 (BatchN (None, 17, 17, 192)  576         conv2d_312[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_319 (BatchN (None, 17, 17, 192)  576         conv2d_315[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_324 (BatchN (None, 17, 17, 192)  576         conv2d_320[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_325 (BatchN (None, 17, 17, 192)  576         conv2d_321[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_316 (Activation)     (None, 17, 17, 192)  0           batch_normalization_316[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_319 (Activation)     (None, 17, 17, 192)  0           batch_normalization_319[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_324 (Activation)     (None, 17, 17, 192)  0           batch_normalization_324[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_325 (Activation)     (None, 17, 17, 192)  0           batch_normalization_325[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 17, 17, 768)  0           activation_316[0][0]             \n",
      "                                                                 activation_319[0][0]             \n",
      "                                                                 activation_324[0][0]             \n",
      "                                                                 activation_325[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_326 (Conv2D)             (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_330 (BatchN (None, 17, 17, 160)  480         conv2d_326[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_330 (Activation)     (None, 17, 17, 160)  0           batch_normalization_330[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_327 (Conv2D)             (None, 17, 17, 160)  179200      activation_330[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_331 (BatchN (None, 17, 17, 160)  480         conv2d_327[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_331 (Activation)     (None, 17, 17, 160)  0           batch_normalization_331[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_323 (Conv2D)             (None, 17, 17, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_328 (Conv2D)             (None, 17, 17, 160)  179200      activation_331[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_327 (BatchN (None, 17, 17, 160)  480         conv2d_323[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_332 (BatchN (None, 17, 17, 160)  480         conv2d_328[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_327 (Activation)     (None, 17, 17, 160)  0           batch_normalization_327[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_332 (Activation)     (None, 17, 17, 160)  0           batch_normalization_332[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_324 (Conv2D)             (None, 17, 17, 160)  179200      activation_327[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_329 (Conv2D)             (None, 17, 17, 160)  179200      activation_332[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_328 (BatchN (None, 17, 17, 160)  480         conv2d_324[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_333 (BatchN (None, 17, 17, 160)  480         conv2d_329[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_328 (Activation)     (None, 17, 17, 160)  0           batch_normalization_328[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_333 (Activation)     (None, 17, 17, 160)  0           batch_normalization_333[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_31 (AveragePo (None, 17, 17, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_322 (Conv2D)             (None, 17, 17, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_325 (Conv2D)             (None, 17, 17, 192)  215040      activation_328[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_330 (Conv2D)             (None, 17, 17, 192)  215040      activation_333[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_331 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_31[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_326 (BatchN (None, 17, 17, 192)  576         conv2d_322[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_329 (BatchN (None, 17, 17, 192)  576         conv2d_325[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_334 (BatchN (None, 17, 17, 192)  576         conv2d_330[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_335 (BatchN (None, 17, 17, 192)  576         conv2d_331[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_326 (Activation)     (None, 17, 17, 192)  0           batch_normalization_326[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_329 (Activation)     (None, 17, 17, 192)  0           batch_normalization_329[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_334 (Activation)     (None, 17, 17, 192)  0           batch_normalization_334[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_335 (Activation)     (None, 17, 17, 192)  0           batch_normalization_335[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 17, 17, 768)  0           activation_326[0][0]             \n",
      "                                                                 activation_329[0][0]             \n",
      "                                                                 activation_334[0][0]             \n",
      "                                                                 activation_335[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_336 (Conv2D)             (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_340 (BatchN (None, 17, 17, 160)  480         conv2d_336[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_340 (Activation)     (None, 17, 17, 160)  0           batch_normalization_340[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_337 (Conv2D)             (None, 17, 17, 160)  179200      activation_340[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_341 (BatchN (None, 17, 17, 160)  480         conv2d_337[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_341 (Activation)     (None, 17, 17, 160)  0           batch_normalization_341[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_333 (Conv2D)             (None, 17, 17, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_338 (Conv2D)             (None, 17, 17, 160)  179200      activation_341[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_337 (BatchN (None, 17, 17, 160)  480         conv2d_333[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_342 (BatchN (None, 17, 17, 160)  480         conv2d_338[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_337 (Activation)     (None, 17, 17, 160)  0           batch_normalization_337[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_342 (Activation)     (None, 17, 17, 160)  0           batch_normalization_342[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_334 (Conv2D)             (None, 17, 17, 160)  179200      activation_337[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_339 (Conv2D)             (None, 17, 17, 160)  179200      activation_342[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_338 (BatchN (None, 17, 17, 160)  480         conv2d_334[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_343 (BatchN (None, 17, 17, 160)  480         conv2d_339[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_338 (Activation)     (None, 17, 17, 160)  0           batch_normalization_338[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_343 (Activation)     (None, 17, 17, 160)  0           batch_normalization_343[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_32 (AveragePo (None, 17, 17, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_332 (Conv2D)             (None, 17, 17, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_335 (Conv2D)             (None, 17, 17, 192)  215040      activation_338[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_340 (Conv2D)             (None, 17, 17, 192)  215040      activation_343[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_341 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_32[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_336 (BatchN (None, 17, 17, 192)  576         conv2d_332[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_339 (BatchN (None, 17, 17, 192)  576         conv2d_335[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_344 (BatchN (None, 17, 17, 192)  576         conv2d_340[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_345 (BatchN (None, 17, 17, 192)  576         conv2d_341[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_336 (Activation)     (None, 17, 17, 192)  0           batch_normalization_336[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_339 (Activation)     (None, 17, 17, 192)  0           batch_normalization_339[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_344 (Activation)     (None, 17, 17, 192)  0           batch_normalization_344[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_345 (Activation)     (None, 17, 17, 192)  0           batch_normalization_345[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 17, 17, 768)  0           activation_336[0][0]             \n",
      "                                                                 activation_339[0][0]             \n",
      "                                                                 activation_344[0][0]             \n",
      "                                                                 activation_345[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_346 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_350 (BatchN (None, 17, 17, 192)  576         conv2d_346[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_350 (Activation)     (None, 17, 17, 192)  0           batch_normalization_350[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_347 (Conv2D)             (None, 17, 17, 192)  258048      activation_350[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_351 (BatchN (None, 17, 17, 192)  576         conv2d_347[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_351 (Activation)     (None, 17, 17, 192)  0           batch_normalization_351[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_343 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_348 (Conv2D)             (None, 17, 17, 192)  258048      activation_351[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_347 (BatchN (None, 17, 17, 192)  576         conv2d_343[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_352 (BatchN (None, 17, 17, 192)  576         conv2d_348[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_347 (Activation)     (None, 17, 17, 192)  0           batch_normalization_347[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_352 (Activation)     (None, 17, 17, 192)  0           batch_normalization_352[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_344 (Conv2D)             (None, 17, 17, 192)  258048      activation_347[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_349 (Conv2D)             (None, 17, 17, 192)  258048      activation_352[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_348 (BatchN (None, 17, 17, 192)  576         conv2d_344[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_353 (BatchN (None, 17, 17, 192)  576         conv2d_349[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_348 (Activation)     (None, 17, 17, 192)  0           batch_normalization_348[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_353 (Activation)     (None, 17, 17, 192)  0           batch_normalization_353[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_33 (AveragePo (None, 17, 17, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_342 (Conv2D)             (None, 17, 17, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_345 (Conv2D)             (None, 17, 17, 192)  258048      activation_348[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_350 (Conv2D)             (None, 17, 17, 192)  258048      activation_353[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_351 (Conv2D)             (None, 17, 17, 192)  147456      average_pooling2d_33[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_346 (BatchN (None, 17, 17, 192)  576         conv2d_342[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_349 (BatchN (None, 17, 17, 192)  576         conv2d_345[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_354 (BatchN (None, 17, 17, 192)  576         conv2d_350[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_355 (BatchN (None, 17, 17, 192)  576         conv2d_351[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_346 (Activation)     (None, 17, 17, 192)  0           batch_normalization_346[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_349 (Activation)     (None, 17, 17, 192)  0           batch_normalization_349[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_354 (Activation)     (None, 17, 17, 192)  0           batch_normalization_354[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_355 (Activation)     (None, 17, 17, 192)  0           batch_normalization_355[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 17, 17, 768)  0           activation_346[0][0]             \n",
      "                                                                 activation_349[0][0]             \n",
      "                                                                 activation_354[0][0]             \n",
      "                                                                 activation_355[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_354 (Conv2D)             (None, 17, 17, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_358 (BatchN (None, 17, 17, 192)  576         conv2d_354[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_358 (Activation)     (None, 17, 17, 192)  0           batch_normalization_358[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_355 (Conv2D)             (None, 17, 17, 192)  258048      activation_358[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_359 (BatchN (None, 17, 17, 192)  576         conv2d_355[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_359 (Activation)     (None, 17, 17, 192)  0           batch_normalization_359[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_352 (Conv2D)             (None, 17, 17, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_356 (Conv2D)             (None, 17, 17, 192)  258048      activation_359[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_356 (BatchN (None, 17, 17, 192)  576         conv2d_352[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_360 (BatchN (None, 17, 17, 192)  576         conv2d_356[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_356 (Activation)     (None, 17, 17, 192)  0           batch_normalization_356[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_360 (Activation)     (None, 17, 17, 192)  0           batch_normalization_360[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_353 (Conv2D)             (None, 8, 8, 320)    552960      activation_356[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_357 (Conv2D)             (None, 8, 8, 192)    331776      activation_360[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_357 (BatchN (None, 8, 8, 320)    960         conv2d_353[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_361 (BatchN (None, 8, 8, 192)    576         conv2d_357[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_357 (Activation)     (None, 8, 8, 320)    0           batch_normalization_357[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_361 (Activation)     (None, 8, 8, 192)    0           batch_normalization_361[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling2D) (None, 8, 8, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 8, 8, 1280)   0           activation_357[0][0]             \n",
      "                                                                 activation_361[0][0]             \n",
      "                                                                 max_pooling2d_15[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_362 (Conv2D)             (None, 8, 8, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_366 (BatchN (None, 8, 8, 448)    1344        conv2d_362[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_366 (Activation)     (None, 8, 8, 448)    0           batch_normalization_366[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_359 (Conv2D)             (None, 8, 8, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_363 (Conv2D)             (None, 8, 8, 384)    1548288     activation_366[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_363 (BatchN (None, 8, 8, 384)    1152        conv2d_359[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_367 (BatchN (None, 8, 8, 384)    1152        conv2d_363[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_363 (Activation)     (None, 8, 8, 384)    0           batch_normalization_363[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_367 (Activation)     (None, 8, 8, 384)    0           batch_normalization_367[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_360 (Conv2D)             (None, 8, 8, 384)    442368      activation_363[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_361 (Conv2D)             (None, 8, 8, 384)    442368      activation_363[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_364 (Conv2D)             (None, 8, 8, 384)    442368      activation_367[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_365 (Conv2D)             (None, 8, 8, 384)    442368      activation_367[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_34 (AveragePo (None, 8, 8, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_358 (Conv2D)             (None, 8, 8, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_364 (BatchN (None, 8, 8, 384)    1152        conv2d_360[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_365 (BatchN (None, 8, 8, 384)    1152        conv2d_361[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_368 (BatchN (None, 8, 8, 384)    1152        conv2d_364[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_369 (BatchN (None, 8, 8, 384)    1152        conv2d_365[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_366 (Conv2D)             (None, 8, 8, 192)    245760      average_pooling2d_34[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_362 (BatchN (None, 8, 8, 320)    960         conv2d_358[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_364 (Activation)     (None, 8, 8, 384)    0           batch_normalization_364[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_365 (Activation)     (None, 8, 8, 384)    0           batch_normalization_365[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_368 (Activation)     (None, 8, 8, 384)    0           batch_normalization_368[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_369 (Activation)     (None, 8, 8, 384)    0           batch_normalization_369[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_370 (BatchN (None, 8, 8, 192)    576         conv2d_366[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_362 (Activation)     (None, 8, 8, 320)    0           batch_normalization_362[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 8, 8, 768)    0           activation_364[0][0]             \n",
      "                                                                 activation_365[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)     (None, 8, 8, 768)    0           activation_368[0][0]             \n",
      "                                                                 activation_369[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_370 (Activation)     (None, 8, 8, 192)    0           batch_normalization_370[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 8, 8, 2048)   0           activation_362[0][0]             \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_6[0][0]              \n",
      "                                                                 activation_370[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_371 (Conv2D)             (None, 8, 8, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_375 (BatchN (None, 8, 8, 448)    1344        conv2d_371[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_375 (Activation)     (None, 8, 8, 448)    0           batch_normalization_375[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_368 (Conv2D)             (None, 8, 8, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_372 (Conv2D)             (None, 8, 8, 384)    1548288     activation_375[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_372 (BatchN (None, 8, 8, 384)    1152        conv2d_368[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_376 (BatchN (None, 8, 8, 384)    1152        conv2d_372[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_372 (Activation)     (None, 8, 8, 384)    0           batch_normalization_372[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_376 (Activation)     (None, 8, 8, 384)    0           batch_normalization_376[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_369 (Conv2D)             (None, 8, 8, 384)    442368      activation_372[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_370 (Conv2D)             (None, 8, 8, 384)    442368      activation_372[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_373 (Conv2D)             (None, 8, 8, 384)    442368      activation_376[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_374 (Conv2D)             (None, 8, 8, 384)    442368      activation_376[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_35 (AveragePo (None, 8, 8, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_367 (Conv2D)             (None, 8, 8, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_373 (BatchN (None, 8, 8, 384)    1152        conv2d_369[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_374 (BatchN (None, 8, 8, 384)    1152        conv2d_370[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_377 (BatchN (None, 8, 8, 384)    1152        conv2d_373[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_378 (BatchN (None, 8, 8, 384)    1152        conv2d_374[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_375 (Conv2D)             (None, 8, 8, 192)    393216      average_pooling2d_35[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_371 (BatchN (None, 8, 8, 320)    960         conv2d_367[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_373 (Activation)     (None, 8, 8, 384)    0           batch_normalization_373[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_374 (Activation)     (None, 8, 8, 384)    0           batch_normalization_374[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_377 (Activation)     (None, 8, 8, 384)    0           batch_normalization_377[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_378 (Activation)     (None, 8, 8, 384)    0           batch_normalization_378[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_379 (BatchN (None, 8, 8, 192)    576         conv2d_375[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_371 (Activation)     (None, 8, 8, 320)    0           batch_normalization_371[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 8, 8, 768)    0           activation_373[0][0]             \n",
      "                                                                 activation_374[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_7 (Concatenate)     (None, 8, 8, 768)    0           activation_377[0][0]             \n",
      "                                                                 activation_378[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_379 (Activation)     (None, 8, 8, 192)    0           batch_normalization_379[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 8, 8, 2048)   0           activation_371[0][0]             \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_7[0][0]              \n",
      "                                                                 activation_379[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_9 (Glo (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_18 (Dense)                (None, 1024)         2098176     global_average_pooling2d_9[0][0] \n",
      "__________________________________________________________________________________________________\n",
      "dense_19 (Dense)                (None, 1)            1025        dense_18[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_2 (Reshape)             (None, 1, 1, 1)      0           dense_19[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "multiply_2 (Multiply)           (None, 1, 1, 1024)   0           dense_18[0][0]                   \n",
      "                                                                 reshape_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda (Lambda)                 (None, 1, 1024)      0           multiply_2[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lambda_1 (Lambda)               (None, 1, 1024, 1)   0           lambda[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_10 (Gl (None, 1)            0           lambda_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_20 (Dense)                (None, 7)            14          global_average_pooling2d_10[0][0]\n",
      "==================================================================================================\n",
      "Total params: 23,901,999\n",
      "Trainable params: 2,099,215\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "\n",
    "# 绘制训练历史\n",
    "plot_history(history_attention, 'Attention Model')"
   ],
   "id": "acc7c86a23cbef46",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.317211200Z",
     "start_time": "2024-12-15T13:10:38.939342Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_33364\\993536772.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[1;31m# 绘制训练历史\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m----> 2\u001B[1;33m \u001B[0mplot_history\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mhistory_attention\u001B[0m\u001B[1;33m,\u001B[0m \u001B[1;34m'Attention Model'\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m: name 'plot_history' is not defined"
     ]
    }
   ],
   "execution_count": 1,
   "source": "explain_image(img, attention_model)",
   "id": "23549f1a8ab6b572"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-15T18:29:17.317211200Z",
     "start_time": "2024-12-15T08:24:38.797132Z"
    }
   },
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 40,
   "source": "attention_model.save('attention_model.h5')",
   "id": "5d9b4be38190db30"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": [
    "# 获取每个模型的输出\n",
    "attention_output = attention_model.output\n",
    "inception_output = inception_model.output\n",
    "resnet_output = resnet_model.output\n",
    "depthwise_separable_output = depthwise_separable_model.output\n",
    "\n",
    "# 将输出拼接起来\n",
    "merged_output = concatenate([attention_output, inception_output, resnet_output, depthwise_separable_output])\n",
    "\n",
    "# 添加全连接层进行分类\n",
    "x = Dense(1024, activation='relu')(merged_output)\n",
    "predictions = Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "# 构建最终的多输入模型\n",
    "final_model = Model(inputs=[attention_model.input, inception_model.input, resnet_model.input, depthwise_separable_model.input], outputs=predictions)\n",
    "\n",
    "# 编译模型\n",
    "final_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# 训练模型\n",
    "print(\"Training Integrated model...\")\n",
    "history_integrated = final_model.fit(\n",
    "    [train_generator, train_generator, train_generator, train_generator],  # 注意这里需要提供四个数据生成器\n",
    "    steps_per_epoch=train_generator.samples // train_generator.batch_size,\n",
    "    validation_data=([validation_generator, validation_generator, validation_generator, validation_generator], validation_generator),\n",
    "    validation_steps=validation_generator.samples // validation_generator.batch_size,\n",
    "    epochs=10\n",
    ")"
   ],
   "id": "d2e9461607c9218b"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "final_model.summary()",
   "id": "3eb92d1a6cd0203f"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "plot_history(history_integrated, 'Integrated Model')",
   "id": "e6fb8ee76327b50e"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "explain_image(img, final_model)",
   "id": "6622ebd5a57691a3"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "final_model.save('integrated_model.h5')",
   "id": "7ddc02a85973c1a9"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "#XAI评测\n",
   "id": "2b334cafd8e82a46"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
